SEI+II Information Integration through Events

许多环境观测是在不同的空间和时间尺度上收集的，这妨碍了数据的整合，并妨碍了对生态系统动态的更广泛理解。海洋观测系统是多传感器系统观测不同时空范围内若干变量的一个具体例子。该项目集成了不同的时空环境传感器流的基础上，他们的信息内容转换为一个共同的更高层次的抽象：时空事件数据类型。时空事件数据类型在观测级别数据的多样性上进行标准化，以产生用于探索和分析的通用数据类型。缅因州湾海洋观测系统（GOMOOS）数据提供了多元时间和时空序列，从中检测和组装时空事件。事件检测采用组合的自上而下-自下而上方法。自上而下的组件指定一个事件本体，而自下而上的组件是基于从时间和空间-时间序列中提取原始事件（例如，减少，增加，局部最大值和最小值序列）。对提取出的事件进行探索和分析时，采用了一种图形探索环境，该环境基于一种称为事件带的图形基元，并将其组合成支持各种时空模式调查的事件带堆栈和面板。该项目提供了一种新的基于事件概念的信息集成方法，该方法可以扩展到许多领域，包括社会经济、金融、立法、监测和健康相关信息。该项目将提供新的数据挖掘战略，用于在时间和空间-时间序列中发现事件，并提供一套灵活的探索工具，用于审查和发展关于空间-时间事件模式和相互作用的假设。

Many environmental observations are collected at different space and time scales that preclude easy integration of the data and hinder broader understanding of ecosystem dynamics. Ocean Observing Systems provide a specific example of multi-sensor systems observing several variables in different space - time regimes. This project integrates diverse space-time environmental sensor streams based on the conversion of their information content to a common higher-level abstraction: a space-time event data type. The space-time event data type normalizes across the diversity of observation level data to produce a common data type for exploration and analysis. Gulf of Maine Ocean Observing System (GOMOOS) data provide the multivariate time and space-time series from which space-time events are detected and assembled. Event detection employs a combined top down-bottom up approach. The top down component specifies an event ontology while the bottom up component is based on extraction of primitive events (e.g. decreasing, increasing, local maxima and minima sequences) from time and space-time series. Exploration and analysis of the extracted events employs a graphic exploratory environment based on a graphic primitive called an event band and its composition into event band stacks and panels that support investigation of various space-time patterns.The project contributes a new information integration approach based on the concept of an event that can be extended to many domains including socio-economic, financial, legislative, surveillance and health related information. The project will contribute new data mining strategies for event detection in time and space-time series and a set of flexible exploratory tools for examination and development of hypotheses on space-time event patterns and interactions.